### 1.1 Business Data Landscape The **uciml/iris** dataset, sourced from Kaggle, is a compact botanical reference collection. It is organized as a single table, **Iris**, which holds 150 rows, each describing one flower specimen. Based on the dataset's name and its classification as a dimension table, the core entity is the iris specimen, which would carry descriptive attributes used to tell specimens apart and group them by type. The structure is deliberately simple: one self-contained table, with no supporting or transactional tables around it. The footprint is small, about 0.2 MB, which makes it fast to load, inspect, and share. ### 1.2 Key Data Assets | Table | Role | Rows | % of Total | Size | |-------|------|-----:|----------:|-----:| | Iris | dimension | 150 | 100.0% | 0.2 MB | **Iris** is the entire dataset, so it holds 100% of all rows. It works as a reference catalog of specimens, and its value comes from the consistency of a single, complete record set rather than from volume. The headline scores reflect this: overall quality, completeness, and referential integrity each stand at 100%. The referential integrity score is a consequence of there being no cross-table dependencies to break, not a sign of a tested network of relationships. ### 1.3 Data Integration Posture Nettle detected **no validated joins**, which is expected for a one-table dataset. This should not be read as fragmentation, because nothing here is split apart or isolated from related data. For analytics, the practical implication is that every question can be answered from **Iris** alone, with no cross-table reconciliation needed. Future value is more likely to come from enrichment, such as adding context about specimen origin or collection, than from repairing connections that do not exist. Extending the schema in this way would be a deliberate step rather than a remediation.